%0 Journal Article
%T Detecting tag spam in social tagging systems with kernel K-meansclustering and semi-definite programming SVM<br>用核K-means聚类和半定规划SVM实现垃圾标签检测
%A QIN Hu
%A DING Li-duo
%A FU Li-jin
%A QIN Xi
%A <br>覃　华
%A 丁立朵
%A 符丽锦
%A 覃　希
%J 计算机应用研究
%D 2013
%I 
%X This paper presented a method. It used kernel K-means clustering algorithm to extract the character vector set from the samples and got the optimal combinatorial coefficients of different functions to construct semi-definite programming SVM with stronger nonlinear mapping ability. Experimental results on UCI datasets show that compared with double-layer reduction method, the new method gives higher accuracy and speeds up obviously.
%K tag spam detection
%K SVM
%K combination of multiple kernel functions
%K semi-definite programming<br>垃圾标签识别
%K 支持向量机
%K 多核函数组合
%K 半定规划
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=A9D9BE08CDC44144BE8B5685705D3AED&aid=DD8ECC43358E540D2FD7BE65B592BDA5&yid=FF7AA908D58E97FA&vid=340AC2BF8E7AB4FD&iid=E158A972A605785F&sid=4206C58D935377EA&eid=7CCBDF94263DBB99&journal_id=1001-3695&journal_name=计算机应用研究&referenced_num=0&reference_num=20